AI automation for insurance: a practical guide
How insurance teams automate claims intake, document chasing, renewals, and policyholder updates, and how to build those automations yourself without waiting on a developer.
Amman Vedi10 min read
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Insurance is a promise that gets tested at the worst moment in a customer's year. Someone's house has flooded or their van has been written off, and the experience they have from that point is almost entirely about information: whether it was collected properly, whether it moved to the right person, and whether anyone told them what was happening.
That makes insurance unusual among the industries worth automating. The manual work isn't just expensive for the business; it's the direct cause of the thing policyholders complain about most. This guide covers what the claims data shows, what insurance teams automate, and how to build those automations without waiting on a development queue.
What we'll cover
What the claims cycle actually looks like
J.D. Power's 2025 U.S. Property Claims Satisfaction Study, based on responses from 5,178 homeowner insurance customers who had filed a claim, fielded across 2024, put numbers on how long the process now takes.
The average cycle from filing a claim to finished repairs reached 32.4 days. The average time from first notice of loss to final payment passed 44 days. Both are the longest since the study began in 2008.
J.D. Power's own reading of the data is the part worth dwelling on: the damage to satisfaction comes less from the duration itself than from what happens during it. A claimant who understands where their claim stands tolerates a long process considerably better than one who hears nothing for three weeks and assumes they've been forgotten.
That's a communication problem sitting inside an operational one, and it's precisely the kind of problem automation addresses well. Nobody chooses to leave a policyholder in the dark. It happens because the adjuster handling their claim is handling eighty others, and proactive updates lose to whatever is escalating today.
Nine automations insurance teams build
Each one follows the same shape: something happens, information moves, and a person picks up a decision that's ready to be made.
1. First notice of loss intake
At a glance: a reported claim arrives structured, categorized, and routed, whatever channel it came in on.
Claims are reported by phone, email, portal, and broker, in wildly varying levels of completeness. An automation that extracts the details into a consistent structure, identifies the policy, checks it was in force, and routes to the right handler means the claim starts moving on day one rather than after someone gets to it.
2. Claim documentation chasing
At a glance: outstanding evidence is tracked per claim and chased without a mental list.
Photographs, repair estimates, police reports, receipts, and medical records arrive from different parties at different times. Tracking what each claim still needs and following up on schedule removes one of the largest contributors to the 44-day figure above, because a claim waiting on a document nobody chased is a claim not moving at all.
3. Proactive claim status updates
At a glance: policyholders hear where their claim stands on a schedule, without an adjuster writing each message.
This is the direct answer to the J.D. Power finding. An automation that tracks each claim's stage and sends an appropriate update, including when the honest update is that the assessor's report is still awaited, changes the experience without changing the cycle time.
Review anything going to a claimant who's had a serious loss. Tone matters more here than in any other industry on this list.
4. Underwriting submission triage
At a glance: submissions arrive with the data extracted and the obvious gaps flagged.
New business submissions come as forms, spreadsheets, and broker emails. Extracting the risk details into a consistent structure and flagging missing information before it reaches an underwriter means underwriting time goes on assessing risk rather than assembling it.
The appetite decision stays with the underwriter. This prepares the question; it doesn't answer it.
5. Renewal preparation
At a glance: renewals surface early with the data already assembled.
Renewal dates are known months in advance, and renewals still routinely get worked in the final week. An automation that surfaces upcoming renewals with claims history, exposure changes, and current terms attached turns a deadline scramble into a scheduled review.
6. Compliance and regulatory reporting
At a glance: periodic returns assemble from source data on schedule.
Regulatory reporting is defined, repetitive, and consequential when late. Assembling the data on a schedule and flagging anomalies before submission reduces both the effort and the risk, though the submission itself stays with the person accountable for it.
7. Broker and policyholder query handling
At a glance: routine questions get immediate answers, and the rest reach a person faster.
A large share of inbound contact is status questions and document requests. Handling those automatically frees the phones for the conversations that need judgment, which is where an experienced handler adds value.
8. Fraud indicator flagging
At a glance: claims matching known patterns are flagged for human review.
Pattern flagging is something software does well and tirelessly. The critical design point is that flagging is not deciding. A flagged claim goes to an investigator, and the policyholder experiences no difference unless and until a person decides there's a case to examine.
9. Subrogation and recovery tracking
At a glance: claims with a recovery prospect are identified and pursued instead of quietly written off.
When a loss was somebody else's fault, the insurer can recover. In practice recovery opportunities are missed routinely, not because anyone decided against pursuing them but because identifying them requires someone to read a claim file with recovery in mind, and then to chase a third party over months while newer claims arrive.
An automation that screens settled claims for recovery indicators, opens a tracked file, and maintains the follow-up schedule turns leakage into revenue. It's one of the few automations on this list with a direct, measurable effect on the loss ratio, which makes it unusually easy to justify internally.
Why good ideas stall before they get built
The gap between knowing what to automate and having it built is wider in insurance than almost anywhere, because the systems involved are older and the change control is heavier.
The developer queue. In a carrier, it exists and it is long, and it is occupied with core system replacement. A claims operations improvement rarely outranks that.
The specialist queue. A consultancy will build it, on a timeline and budget that make small improvements uneconomic. Brokers and MGAs often can't reach this queue at all.
The vendor queue. The request goes to the policy administration vendor and joins a roadmap measured in years.
CodeWords is built for the person stuck in those queues. You describe the outcome you want in plain language, and Cody, the automation builder, takes it from there: building the automation, connecting it to the tools you already use, and deploying it. The person who understands how claims actually move is the person who builds it.
When the process changes, you describe the change. Insurance processes change with every regulatory update, every new product, and every scheme the business takes on. A change you can describe in a sentence doesn't need to become a project.
Automations connect to more than 3,000 integrations, covering the email, document, spreadsheet, and messaging tools that surround the core systems in most insurance businesses. The free plan covers light use, with Pro at $39 per month and Business at $100 per month as usage grows. Current details are on the pricing page.
The boundary matters more in insurance than in most industries, so it's worth stating plainly: these automations collect, route, prepare, and inform, but they don't decide. Coverage decisions, claim settlements, declinatures, and pricing carry regulatory obligations and affect people at vulnerable moments. Those belong to a qualified person, and in many jurisdictions treating-customers-fairly rules mean an automated decision affecting a policyholder needs a documented human review path.
How to build your first insurance automation
Start with claim status updates. It addresses the best-evidenced complaint in the industry, it touches no decision-making, and policyholders notice within a week.
Write the process down as you'd explain it to a new handler. What starts it, what information is needed, where it lives, what the decision points are, and what the policyholder should receive. That description is itself the input, so there's no second step where someone translates it.
Name the exceptions. The large loss that needs an individual conversation. The vulnerable customer who needs a phone call rather than an email. The claim in dispute where nothing automated should be sent. Describing when the automation should stand down is what makes it safe to rely on.
Run it on one claim type before extending. A single line of business is a complete test and a contained risk.
Then take the next one. Documentation chasing is a natural second, because it uses the same claim data with a different trigger.
Frequently asked questions
Are we allowed to automate anything that affects a policyholder?
That depends on your jurisdiction and what "affects" means, so it's worth getting a clear internal answer before building rather than after. Broadly, communication, collection, and preparation are treated very differently from decisions on coverage, settlement, or price. Many regulators expect a documented human review path for automated decisions affecting customers, and expectations around vulnerable customers are more demanding again. The safe design is to automate everything up to the decision and keep the decision with a person.
Will this work with our policy administration system?
Usually alongside it rather than inside it. Core insurance systems are typically old, heavily customized, and slow to change, which is exactly why the manual work accumulates around them in email, spreadsheets, and shared drives. That surrounding layer is where most of the achievable improvement sits and where automation is easiest to introduce safely.
We're a broker, not a carrier. Does this apply?
Yes, often more so. Brokers carry a large share of the chasing and coordination while having the least access to the development resources that could reduce it. Renewal preparation, document chasing, and query handling are the usual starting points.
What about claims data and privacy?
Insurance claims contain some of the most sensitive data any business handles, including health information and financial detail. Consider where data is processed and stored, which services each automation touches, what your regulator requires, and what your privacy notices already commit you to. Starting with internal process automation, such as renewal surfacing and reporting, avoids the sensitive categories while you build confidence.
Does automating claims updates feel impersonal at a difficult moment?
The comparison that matters is timely against silent. The current alternative for most claimants is no contact at all, which is what drives the satisfaction figures above. The current alternative for most claimants is no contact at all, which is what drives the satisfaction figures above. Keeping a review step on communications for serious losses gets both: regular contact, with a person's judgment on what to say when it's bad news.
Does this replace adjusters or handlers?
The pattern is a change in the mix. What automates well is intake, chasing, routing, and status communication. What remains is assessment, negotiation, and the judgment calls on complex or disputed claims, which is where experience actually shows.
How long does it take to build one?
A straightforward automation can be built and deployed in a single sitting. In insurance the realistic constraint is usually the internal approval for touching a regulated process, which is worth starting before the build rather than after.
How do we get this past our compliance and risk functions?
Bring them in before you build rather than presenting a finished automation for approval, which is where these efforts usually die. The argument that tends to land is that automation makes a process more auditable, not less: a defined sequence that runs the same way every time and logs what it did is easier to evidence to a regulator than a manual process that depends on which handler picked it up.
Start with something that touches no decision, such as status updates or renewal surfacing. A working example that demonstrably improved a customer outcome is a far better basis for the next conversation than a proposal.
What happens if the automation gets something wrong?
It's better to design for that than to hope it doesn't happen. The practical protections are keeping decisions with people, building a review step on anything customer-facing, and making sure exceptions route to a person rather than failing silently. Worth deciding up front what the automation should do when it isn't confident, because "stop and escalate" is almost always the right answer in a regulated process and rarely the default.
Related reading
- AI automation for accounting firms
- AI document processing platform
- AI automation examples
- CodeWords integrations and templates
Forty-four days is survivable for most people. Forty-four days of silence is what customers remember.